Bayesian Disease Mapping to Identify High-Risk Population for Oral Cancer: A Retrospective Spatiotemporal Analysis

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Public Health

Background:

• Oral cancer presents a significant public health challenge globally and in Australia.
• Identifying geographic patterns of oral cancer incidence and mortality is crucial for effective resource allocation.
• Bayesian disease mapping is a powerful spatiotemporal tool for identifying disease hotspots.

Purpose of the Study:

• To apply Bayesian disease mapping to identify oral cancer incidence and mortality hotspots in Queensland, Australia.
• To analyze oral cancer trends and spatial patterns within Queensland's Local Government Areas (LGAs) and suburbs.
• To provide data-driven insights for targeted public health interventions in high-risk areas.

Main Methods:

• Retrospective study using data from the Queensland state Cancer Registry (1982-2018).
• Employed Bayesian mapping to estimate smoothed incidence risk (SIR) and smoothed mortality risk (SMR) for oral cancer.
• Utilized Local Government Areas (LGAs) and suburbs as geographical units for analysis.

Main Results:

• Twenty-one of 78 LGAs exhibited higher than average oral cancer incidence.
• Nine rural LGAs showed incidence risk above the state average, with Mount Isa City having consistently high risk.
• Thirty-one LGAs had significantly higher than average oral cancer mortality risk, with Yarrabah Aboriginal Shire, Cook Shire, and Mount Isa City identified as having the highest risks.

Conclusions:

• Bayesian disease mapping effectively identified multiple oral cancer incidence and mortality hotspots in regional Queensland.
• These findings can inform the development of targeted public health screening and primary prevention programs.
• Focusing interventions on identified regional and remote hotspots can improve oral cancer outcomes.